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      Python+OpenCV教程番外篇1：代码性能优化
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	Posted on <a href="/opencv-python-extra-code-optimization/" class="article-date">
	  <time datetime="2017-12-06T07:51:52.000Z" itemprop="datePublished">December 6, 2017</time>
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        <p>学习如何评估和优化代码性能。（本节还没更新完…………）<a id="more"></a></p>
<p>完成一项任务很重要，高效地完成更重要。图像处理是对矩阵的操作，数据量巨大。如果代码写的不好，性能差距将很大，所以这节我们来了解下如何评估和提升代码性能。</p>
<h2 id="评估代码运行时间"><a href="#评估代码运行时间" class="headerlink" title="评估代码运行时间"></a>评估代码运行时间</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">import</span> cv2</div><div class="line"></div><div class="line">start = cv2.getTickCount()</div><div class="line"><span class="comment"># 这里写测试代码...</span></div><div class="line">end = cv2.getTickCount()</div><div class="line">print((end - start) / cv2.getTickFrequency())</div></pre></td></tr></table></figure>
<p>这段代码就是用来测量程序运行时间的（单位：s），其中<code>cv2.getTickCount()</code>函数得到电脑启动以来的时钟周期数，<code>cv2.getTickFrequency()</code>返回你电脑的主频，前后相减再除以主频就是你代码的运行时间（这样解释并不完全准确，但能理解就行）。另外，也可以用Python中的time模块计时：</p>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">import</span> time</div><div class="line"></div><div class="line">start = time.clock()</div><div class="line"><span class="comment"># 这里写测试代码...</span></div><div class="line">end = time.clock()</div><div class="line">print(end - start)</div></pre></td></tr></table></figure>
<blockquote>
<p>经验之谈：如果你使用的是<a href="https://baike.baidu.com/item/ipython" target="_blank" rel="external">IPython</a>或<a href="https://baike.baidu.com/item/Jupyter" target="_blank" rel="external">Jupyter Notebook</a>开发环境，性能分析将会非常方便，详情请参考：<a href="http://pynash.org/2013/03/06/timing-and-profiling/" target="_blank" rel="external">Timing and Profiling in IPython</a></p>
</blockquote>
<h2 id="优化原则"><a href="#优化原则" class="headerlink" title="优化原则"></a>优化原则</h2><ul>
<li>数据元素少时用Python语法，数据元素多时用Numpy：</li>
</ul>
<figure class="highlight python"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div></pre></td><td class="code"><pre><div class="line">x = <span class="number">10</span></div><div class="line">z = np.uint8([<span class="number">10</span>])</div><div class="line"></div><div class="line"><span class="comment"># 尝试比较下面三句话各自的运行时间</span></div><div class="line">y = x * x * x   <span class="comment"># (1.6410249677846285e-06)</span></div><div class="line">y = x**<span class="number">3</span>        <span class="comment"># (2.461537451676943e-06)</span></div><div class="line">y = z * z * z   <span class="comment"># 最慢 (3.1179474387907945e-05)</span></div></pre></td></tr></table></figure>
<p>所以Numpy的运行速度并不一定比Python本身语法快，元素数量较少时，请用Python本身格式。</p>
<ul>
<li>尽量避免使用循环，尤其嵌套循环，因为极其慢！！！</li>
<li>优先使用OpenCV/Numpy中封装好的函数</li>
<li>尽量将数据向量化，变成Numpy的数据格式</li>
<li>尽量避免数组的复制操作</li>
</ul>
<h2 id="接口文档"><a href="#接口文档" class="headerlink" title="接口文档"></a>接口文档</h2><ul>
<li><a href="https://docs.opencv.org/4.0.0/db/de0/group__core__utils.html#gae73f58000611a1af25dd36d496bf4487" target="_blank" rel="external">cv2.getTickCount()</a></li>
<li><a href="https://docs.opencv.org/4.0.0/db/de0/group__core__utils.html#ga705441a9ef01f47acdc55d87fbe5090c" target="_blank" rel="external">cv2.getTickFrequency()</a></li>
</ul>
<h2 id="引用"><a href="#引用" class="headerlink" title="引用"></a>引用</h2><ul>
<li><a href="https://github.com/ex2tron/OpenCV-Python-Tutorial/tree/master/%E7%95%AA%E5%A4%96%E7%AF%8701.%20%E4%BB%A3%E7%A0%81%E6%80%A7%E8%83%BD%E4%BC%98%E5%8C%96" target="_blank" rel="external">本节源码</a></li>
<li><a href="https://wiki.python.org/moin/PythonSpeed/PerformanceTips" target="_blank" rel="external">Python Optimization Techniques</a></li>
<li><a href="http://pynash.org/2013/03/06/timing-and-profiling/" target="_blank" rel="external">Timing and Profiling in IPython</a></li>
<li><a href="http://www.scipy-lectures.org/advanced/advanced_numpy/index.html#advanced-numpy" target="_blank" rel="external">Advanced Numpy</a></li>
</ul>

      
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